Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network

Design and analysis of a beacon-less routing protocol for large volume content dissemination in vehicular ad hoc networks

Largevolumecontentdisseminationispursuedbythegrowingnumberofhighquality applications for Vehicular Ad hoc NETworks(VANETs), e.g., the live road surveillance service and the video-based overtaking assistant service. For the highly dynamical vehicular network topology, beacon-less routing protocols have been proven to be efficient in achieving a balance between the system performance and the control overhead. However, to the authors’ best knowledge, the routing design for large volume content has not been well considered in the previous work, which will introduce new challenges, e.g., the enhanced connectivity requirement for a radio link. In this paper, a link Lifetime-aware Beacon-less Routing Protocol (LBRP) is designed for large volume content delivery in VANETs. Each vehicle makes the forwarding decision based on the message header information and its current state, including the speed and position information. A semi-Markov process analytical model is proposed to evaluate the expected delay in constructing one routing path for LBRP. Simulations show that the proposed LBRP scheme outperforms the traditional dissemination protocols in providing a low end-to-end delay. The analytical model is shown to exhibit a good match on the delay estimation with Monte Carlo simulations, as well

Quality-Driven Cross-Layer Protocols for Video Streaming over Vehicular Ad-Hoc Networks

The emerging vehicular ad-hoc networks (VANETs) offer a variety of applications and new potential markets related to safety, convenience and entertainment, however, they suffer from a number of challenges not shared so deeply by other types of existing networks, particularly, in terms of mobility of nodes, and end-to-end quality of service (QoS) provision. Although several existing works in the literature have attempted to provide efficient protocols at different layers targeted mostly for safety applications, there remain many barriers to be overcome in order to constrain the widespread use of such networks for non-safety applications, specifically, for video streaming: 1) impact of high speed mobility of nodes on end-to-end QoS provision; 2) cross-layer protocol design while keeping low computational complexity; 3) considering customer-oriented QoS metrics in the design of protocols; and 4) maintaining seamless single-hop and multi-hop connection between the destination vehicle and the road side unit (RSU) while network is moving. This thesis addresses each of the above limitations in design of cross-layer protocols for video streaming application. 1) An adaptive MAC retransmission limit selection scheme is proposed to improve the performance of IEEE 802.11p standard MAC protocol for video streaming applications over VANETs. A multi-objective optimization framework, which jointly minimizes the probability of playback freezes and start-up delay of the streamed video at the destination vehicle by tuning the MAC retransmission limit with respect to channel statistics as well as packet transmission rate, is applied at road side unit (RSU). Two-hop transmission is applied in zones in which the destination vehicle is not within the transmission range of any RSU. In the multi-hop scenario, we discuss the computation of access probability used in the MAC adaptation scheme and propose a cross-layer path selection scheme; 2) We take advantage of similarity between multi-hop urban VANETs in dense traffic conditions and mesh connected networks. First, we investigate an application-centric routing scheme for video streaming over mesh connected overlays. Next, we introduce the challenges of urban VANETs compared to mesh networks and extend the proposed scheme in mesh network into a protocol for urban VANETs. A classification-based method is proposed to select an optimal path for video streaming over multi-hop mesh networks. The novelty is to translate the path selection over multi-hop networks to a standard classification problem. The classification is based on minimizing average video packet distortion at the receiving nodes. The classifiers are trained offline using a vast collection of video sequences and wireless channel conditions in order to yield optimal performance during real time path selection. Our method substantially reduces the complexity of conventional exhaustive optimization methods and results in high quality (low distortion). Next, we propose an application-centric routing scheme for real-time video transmission over urban multi-hop vehicular ad-hoc network (VANET) scenarios. Queuing based mobility model, spatial traffic distribution and prob- ability of connectivity for sparse and dense VANET scenarios are taken into consideration in designing the routing protocol. Numerical results demonstrate the gain achieved by the proposed routing scheme versus geographic greedy forwarding in terms of video frame distortion and streaming start-up delay in several urban communication scenarios for various vehicle entrance rate and traffic densities; and 3) finally, the proposed quality-driven routing scheme for delivering video streams is combined with a novel IP management scheme. The routing scheme aims to optimize the visual quality of the transmitted video frames by minimizing the distortion, the start-up delay, and the frequency of the streaming freezes. As the destination vehicle is in motion, it is unrealistic to assume that the vehicle will remain connected to the same access router (AR) for the whole trip. Mobile IP management schemes can benefit from the proposed multi-hop routing protocol in order to adapt proxy mobile IPv6 (PMIPv6) for multi-hop VANET for video streaming applications. The proposed cross-layer protocols can significantly improve the video streaming quality in terms of the number of streaming freezes and start-up delay over VANETs while achieving low computational complexity by using pattern classification methods for optimization

MADServer: An Architecture for Opportunistic Mobile Advanced Delivery

Rapid increases in cellular data traffic demand creative alternative delivery vectors for data. Despite the conceptual attractiveness of mobile data offloading, no concrete web server architectures integrate intelligent offloading in a production-ready and easily deployable manner without relying on vast infrastructural changes to carriers’ networks. Delay-tolerant networking technology offers the means to do just this. We introduce MADServer, a novel DTN-based architecture for mobile data offloading that splits web con- tent among multiple independent delivery vectors based on user and data context. It enables intelligent data offload- ing, caching, and querying solutions which can be incorporated in a manner that still satisfies user expectations for timely delivery. At the same time, it allows for users who have poor or expensive connections to the cellular network to leverage multi-hop opportunistic routing to send and receive data. We also present a preliminary implementation of MADServer and provide real-world performance evaluations